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Auflistung nach Autor:in "Giunchiglia, Fausto"

1 - 3 von 3
  • Zeitschriftenartikel
    Dealing with Mislabeling via Interactive Machine Learning
    (KI - Künstliche Intelligenz: Vol. 34, No. 2, 2020) Zhang, Wanyi; Passerini, Andrea; Giunchiglia, Fausto
    We propose an interactive machine learning framework where the machine questions the user feedback when it realizes it is inconsistent with the knowledge previously accumulated. The key idea is that the machine uses its available knowledge to check the correctness of its own and the user labeling. The proposed architecture and algorithms run through a series of modes with progressively higher confidence and features a conflict resolution component. The proposed solution is tested in a project on university student life where the goal is to recognize tasks like user location and transportation mode from sensor data. The results highlight the unexpected extreme pervasiveness of annotation mistakes and the advantages provided by skeptical learning.
  • Konferenzbeitrag
    A Methodology and System For Big-Thick Data Collection
    (INFORMATIK 2024, 2024) Kayongo, Ivan; Zhao, Haonan; Malcotti, Leonardo; Giunchiglia, Fausto
    Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion enables effective collaboration between humans and machines, allowing machine learning to benefit from human behavior and interpretations. Emphasizing data quality, our system incorporates continuous monitoring and adaptive learning mechanisms to optimize data collection timing and context, ensuring relevance, accuracy, and reliability. The system comprises three key components: a) a tool for collecting sensor data and user feedback, b) components for experiment planning and execution monitoring, and c) a machine-learning component that enhances human-machine interaction.
  • Konferenzbeitrag
    A multi-agent system for knowledge management based on the implicit culture framework
    (WM 2003: Professionelles Wissesmanagement – Erfahrungen und Visionen, Beiträge der 2. Konferenz Professionelles Wissensmanagement, 2003) Blanzieri, Enrico; Giorgini, Paolo; Giunchiglia, Fausto; Zanoni, Claudio
    We present an implementation of a multi-agent system whose goal is to solve the problem of tacit knowledge transfer by means of sharing of experiences. In particular, we consider experiences of use of pieces of information. Each agent incorporates a systems for implicit culture support (SICS) whose goal is to realize the acceptance of the information suggested. The SICS permits a transparent, namely implicit, sharing of the information about the use, e.g. requesting and accepting, of pieces of information.